Adapting Regularized Low Rank Models for Parallel Architectures

Understand

We introduce a reformulation of regularized low-rank recovery models to take advantage of GPU, multiple CPU, and hybridized architectures.

• Low-rank recovery often involves nuclear-norm minimization through iterative thresholding of singular values.
• These models are slow to fit and difficult to parallelize because of their dependence on computing a singular value decomposition at each iteration.
• Regularized low-rank recovery models also incorporate non-smooth terms to separate structured components (e.g.